combine list of data frames in list in specific manner - r

Question

I got a list which have another list of data frames.
The outside list elements represents years and inside list represent months data.
Now I want to create a final list which will contain data for all months. Each Month columns will be "cbinded" by other years column values.
Alldata <- list()
Alldata[[1]] <- list(data.frame(Jan_2015_A=c(1,2), Jan_2015_B=c(3,4)), data.frame(Feb_2015_C=c(5,6), Feb_2015_D=c(7,8)))
Alldata[[2]] <- list(data.frame(Jan_2016_A=c(1,2), Jan_2016_B=c(3,4)), data.frame(Feb_2016_C=c(5,6), Feb_2016_D=c(7,8)))
Expected output list is as following
I've tried using for loops and its little complex, I want any R function to do this task.
I have done this using for loops using following code. But this is really complex and I myself found this little complicate. Hope I will get any simpler and tidy code for this operation.
I created list with each months and years data as a list item in form of data frames
x2 <- list()
for(l1 in 1: length(Alldata[[1]])){
temp <- list()
for(l2 in 1: length(Alldata)){
temp <- append(temp, list(Alldata[[l2]][[l1]]))
}
x2 <- append(x2, list(temp))
}
# then created final List with succesive years data of each month as list items. This is primarily used for Tracking data for years For Example: how much was count was for Jan_2015 and Jan_2016 for "A"
finalList <- list()
for(l3 in 1: length(x2)){
temp <- x2[[l3]]
td2 <- as.data.frame(matrix("", nrow = nrow(temp[[1]])))
rownames(td2)[rownames(temp[[1]])!=""] <- rownames(temp[[1]])[rownames(temp[[1]])!=""]
for(l4 in 1:ncol(temp[[1]])){
for(l5 in 1: length(temp)){
# lapply(l4, function(x) do.call(cbind,
td2 <- cbind(td2, temp[[l5]][, l4, drop=F])
}
}
finalList <- append(finalList, list(td2))
}
> finalList
[[1]]
V1 Jan_2015_A Jan_2016_A Jan_2015_B Jan_2016_B
1 1 1 3 3
2 2 2 4 4
[[2]]
V1 Feb_2015_C Feb_2016_C Feb_2015_D Feb_2016_D
1 5 5 7 7
2 6 6 8 8

Answer 1

You could do the following below. The lapply will iterate over the outer list and the do.call will cbind the inner list of data frames.
lapply(Alldata, do.call, what = 'cbind')
[[1]]
Jan_2015_A Jan_2015_B Feb_2015_C Feb_2015_D
1 1 3 5 7
2 2 4 6 8
[[2]]
Jan_2016_A Jan_2016_B Feb_2016_C Feb_2016_D
1 1 3 5 7
2 2 4 6 8
You can also use dplyr to get the same results.
library(dplyr)
lapply(Alldata, bind_cols)
Here is a third option proposed by J.R.
lapply(Alldata, Reduce, f = cbind)
EDIT
After clarification from OP, the above solution has been modified (see below) to produce the newly specified output. The solution above has been left there since it is a building block for the solution below.
pattern.vec <- c("Jan", "Feb")
### For a given vector of months/patterns, returns a
### list of elements with only that month.
mon_data <- function(mo) {
return(bind_cols(sapply(Alldata, function(x) { x[grep(pattern = mo, x)]})))
}
### Loop through months/patterns.
finalList <- lapply(pattern.vec, mon_data)
finalList
## [[1]]
## Jan_2015_A Jan_2015_B Jan_2016_A Jan_2016_B
## 1 1 3 1 3
## 2 2 4 2 4
##
## [[2]]
## Feb_2015_C Feb_2015_D Feb_2016_C Feb_2016_D
## 1 5 7 5 7
## 2 6 8 6 8
## Ordering the columns as specified in the original question.
## sorting is by the last character in the column name (A or B)
## and then the year.
lapply(finalList, function(x) x[ order(gsub('[^_]+_([^_]+)_(.*)', '\\2_\\1', colnames(x))) ])
## [[1]]
## Jan_2015_A Jan_2016_A Jan_2015_B Jan_2016_B
## 1 1 1 3 3
## 2 2 2 4 4
##
## [[2]]
## Feb_2015_C Feb_2016_C Feb_2015_D Feb_2016_D
## 1 5 5 7 7
## 2 6 6 8 8